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Location: Dublin, Ireland About Huawei Huawei’s products and services are available in more than 170 countries and are used by a third of the world’s population. Huawei Consumer Business Group (CBG) is one of Huawei’s three business units and covers smartphones, PCs and tablets, wearables and cloud services, etc. Huawei Mobile Services (HMS) is part of CBG and develops new cloud services offered free of charge to Huawei mobile device users. HMS ecosystem is now the third largest ecosystem in the world with more than 96,000 global apps integrated with HMS Core. HMS Apps continues to launch globally, with content apps such as HUAWEI Music, HUAWEI Video, HUAWEI Themes, HUAWEI Reader and HUAWEI Game Center taking centre stage in various countries and regions. About the IRC Huawei Ireland Research Centre's (IRC) mission is to position Huawei as a recognized technology leader and global information and communications technology (ICT) solutions provider. To achieve this we are building an industry-recognized multi-discipline Research Centre of experts focusing on medium-term to long-term issues. The IRC will work closely with an open innovative ecosystem with Huawei customers to address real-world issues. The IRC will also engage with key European universities to build a basic research capability to support Huawei technical projects. About the Job As a Principal Researcher in Personalization and Recommendation at Huawei Ireland Research Centre, you will lead major research workstreams in next generation personalization and recommendation systems. The role sits at the intersection of recommender systems research, large scale sequential modeling, and industrial personalization. You will work on systems that model user behavior across rich interaction streams, learn robust item and event representations, and support high quality personalized experiences across different domains and product scenarios. A central part of the role will be to contribute to Huawei’s roadmap in generative recommendation and next generation personalization. This includes semantic ID representations, transformer based sequential recommendation, efficient attention for long sequences, unified recall and ranking architectures, and principled evaluation of large scale recommendation models. We are looking for a senior researcher who combines hands on modeling experience with strong technical judgment. The successful candidate will own important research workstreams, contribute to technical direction, mentor team members, and translate promising ideas into production relevant systems. Responsibilities - Lead research workstreams for next generation personalization and recommendation systems, with a focus on generative recommendation, large scale sequential modeling, and unified recall and ranking. - Design, develop, and evaluate generative recommendation models that treat recommendation as sequence modeling over user events, items, actions, or semantic identifiers. - Develop and evaluate semantic ID representations, including hierarchical, non hierarchical, graph informed, and learned tokenization approaches. - Investigate long sequence recommendation models capable of using rich user histories and device event streams. - Explore efficient attention mechanisms and scalable transformer architectures for long context recommendation. - Study scaling behavior in recommendation models, including the relationship between model size, data size, sequence length, and downstream performance. - Contribute to the technical roadmap for large scale personalization and recommendation systems. - Translate research ideas into production relevant models, prototypes, technical reports, patents, and deployment proposals. - Design rigorous offline and online evaluations, including ranking metrics, retrieval metrics, calibration, latency, throughput, robustness, and business impact. - Col